Flux 2 Trainer Training

FLUX.2 [dev] Trainer

Custom model specialization through LoRA fine-tuning for text-to-image generation. The FLUX.2 [dev] trainer enables you to teach the model your brand's visual language, specific subjects, artistic styles, or specialized rendering requirements through efficient Low-Rank Adaptation training. Train once, generate infinite variations—your custom model deploys instantly to FLUX.2 [dev] LoRA inference endpoints.

Built for: Brand consistency training | Character design | Custom artistic styles | Product-specific rendering | Domain specialization | Style transfer learning

Training Overview

LoRA (Low-Rank Adaptation) fine-tuning specializes FLUX.2 [dev] without the computational cost of full model retraining. By training on your curated dataset, the model learns to generate images that reflect your specific requirements—whether that's maintaining brand visual standards, rendering particular subjects, or applying custom artistic styles.

What you can train:

• Brand style consistency: Train on your brand's visual guidelines, color palettes, and aesthetic standards for consistent generation across campaigns
• Character generation: Create models that maintain character consistency across diverse scenes, poses, and contexts
• Artistic styles: Teach the model specific artistic approaches, painting techniques, or illustration styles
• Product rendering: Specialize in rendering specific product categories, materials, or presentation styles
• Domain-specific imagery: Technical visualizations, architectural styles, or specialized subject matter

Dataset Preparation

The quality of your training dataset directly determines model performance. Invest time in collecting and preparing high-quality training data.

Image Quality Requirements

• Resolution: Minimum 1024x1024px, higher resolutions preferred
• Quality: No compression artifacts, noise, or degradation
• Consistency: Images should consistently represent the target style or subject
• Quantity: 9-50 images typically sufficient for style training; more for complex subjects

Pro tip: Very high-resolution source images (4K+) can be resized during training, which naturally filters out minor imperfections and compression artifacts.

Dataset Structure

Organize your dataset as a ZIP archive with images and optional caption files:

dataset.zip
├── image_001.png
├── image_001.txt (optional caption)
├── image_002.jpg
├── image_002.txt (optional caption)
├── image_003.png
└── ...

Caption files (`.txt`) should share the same root filename as their corresponding images. If no caption file is provided, the trainer uses the `trigger_word` parameter as the default caption.